A Varying-Parameter Recurrent Neural Network Combined With Penalty Function for Solving Constrained Multi-Criteria Optimization Scheme for Redundant Robot Manipulators

Zhong, Nan; Huang, Qingyu; Song, Yang; Ouyang, Fan; Zhang, Zhijun

doi:10.1109/access.2021.3068731

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…Additionally, the design parameter λ > 0 is a real number that regulates the model's convergence speed. For instance, a greater value for λ will increase the model's convergence speed [45][46][47]. It is important to point out that continual learning is defined as learning continually from non-stationary data while simultaneously transferring and preserving prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Towards Higher-Order Zeroing Neural Networks for Calculating Quaternion Matrix Inverse with Application to Robotic Motion Tracking

et al. 2023

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The efficient solution of the time-varying quaternion matrix inverse (TVQ-INV) is a challenging but crucial topic due to the significance of quaternions in many disciplines, including physics, engineering, and computer science. The main goal of this research is to employ the higher-order zeroing neural network (HZNN) strategy to address the TVQ-INV problem. HZNN is a family of zeroing neural network models that correlates to the hyperpower family of iterative methods with adjustable convergence order. Particularly, three novel HZNN models are created in order to solve the TVQ-INV both directly in the quaternion domain and indirectly in the complex and real domains. The noise-handling version of these models is also presented, and the performance of these models under various types of noises is theoretically and numerically tested. The effectiveness and practicality of these models are further supported by their use in robotic motion tracking. According to the principal results, each of these six models can solve the TVQ-INV effectively, and the HZNN strategy offers a faster convergence rate than the conventional zeroing neural network strategy.

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Section: Introductionmentioning

confidence: 99%

Towards Higher-Order Zeroing Neural Networks for Calculating Quaternion Matrix Inverse with Application to Robotic Motion Tracking

et al. 2023

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“…Artificial neural networks are frequently utilized in science and engineering fields to solve timevarying matrix inversion (TV-MI) problems, including optimization and robot control (Zhong et al, 2021), signal-processing and statistics (Cichocki & Unbehauen, 1993). The dynamic system approach is an essential and efficient parallel processing technique for handling matrix-inversion problems.…”

Section: Introductionmentioning

confidence: 99%

“…As a special kind of RNN, created by Zhang et al in (Zhang & Ge, 2005), the zeroing neural network (ZNN) is considered as a state-of-the-art online computation method. Mainly, as a tool for zeroing equations, ZNN has been thoroughly investigated and used to generate online solutions for time-varying problems in a wide domain of time-varying problems, including problems of matrix equations systems (Jin et al, 2017), quadratic optimization (Zhong et al, 2021), linear equations systems (Stanimirović et al, 2022), and generalized inversion (Katsikis et al, 2022). Declaring an error matrix equation (ERME) 𝐸(𝑡) ∈ ℝ !×!…”

Section: Introductionmentioning

confidence: 99%

A Neutrosophic Adaptive Recurrent Neural Network for Time-Varying Matrix Inversion

Mourtas¹

2023

European Proceedings of Computers and Technology

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Recently, extensive research has been carried out on the convergence and robustness of a unique class of recurrent neural networks known as zeroing neural networks (ZNN). ZNN dynamics has proven to be highly effective at tackling a wide range of time-varying problems in science and engineering. The use of fuzzy logic systems (FLS) in improving ZNN dynamic systems has been shown to be effective in recent research. neutrosophic logic system (NLS) is known to be a more general and efficient approach than FLS. An improvement of the ZNN design for tackling time-varying matrix inversion problems is proposed in this paper by using an appropriately defined NLS. Particularly, a suitable value obtained as the output of an appropriately designed NLS can be used to dynamically adjust the gain parameter contained in ZNN architecture over time to speed up the convergence of the ZNN model. Numerical experiments show that the NLS-based ZNN model is more effective than the equivalent traditional ZNN model in defining the varying-gain parameter.

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Zeroing neural network approaches for computing time-varying minimal rank outer inverse

Stanimirović,

Mourtas,

Mosić

et al. 2024

Applied Mathematics and Computation

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A Varying-Parameter Recurrent Neural Network Combined With Penalty Function for Solving Constrained Multi-Criteria Optimization Scheme for Redundant Robot Manipulators

Cited by 9 publications

References 40 publications

Towards Higher-Order Zeroing Neural Networks for Calculating Quaternion Matrix Inverse with Application to Robotic Motion Tracking

Towards Higher-Order Zeroing Neural Networks for Calculating Quaternion Matrix Inverse with Application to Robotic Motion Tracking

A Neutrosophic Adaptive Recurrent Neural Network for Time-Varying Matrix Inversion

Zeroing neural network approaches for computing time-varying minimal rank outer inverse

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